Interfirm collaboration as a performance-enhancing survival strategy within the business models of ethnic minority-owned urban restaurants affected by COVID-19
Bibliographic record
Abstract
Purpose Guided by resource-based theory, this investigation examines the extent to which knowledge sharing as part of interfirm collaboration serves as a performance-enhancing strategy; that is, in the context of assisting ethnic minority-owned urban restaurants to survive during a major market disruption. Specifically, the study features owner-managers' perceptions concerning the evolving environmental circumstances associated with the novel coronavirus (COVID-19) pandemic. Design/methodology/approach Data collection took place among owner-managers of urban restaurants in a Canadian city during the COVID-19 pandemic in late 2020. This featured semi-structured interviews with restaurants' owner-managers originating from various ethnic origins together with secondary data where possible. Data analysis followed an adapted Gioia approach. Findings Examples of interfirm collaboration include restaurants' owner-managers leveraging social capital and sharing knowledge about the effects of legislation and health guidelines on operating procedures, together with good and bad practices where firms have pivoted their business models via take-outs, patio dining and in-room dining. Irrespective of the strength of network ties (within and across ethnic communities), owner-managers were motivated to share information to facilitate their survival. Nevertheless, this study raises questions over the extent that certain decision-makers exhibit strategic flexibility responding to environmental conditions together with their respective ability to engage/retain customers plus service-oriented employees. In addition, a question is whether some owner-managers will continue to collaborate with their competitors after COVID-19 ends, and if so, with whom and the magnitude of activities. In particular, “trust” via psychological contracts and “complementary strategies” among partners across coethnic and different ethnic origins are key considerations. Originality/value A body of knowledge exists addressing the notions of both interfirm collaboration and market disruptions in the broader cross-disciplinary literature. However, the interfirm collaborative practices of small firms with ethnic minority ownership that are otherwise rivals remain under-researched. More specifically, interfirm collaboration as a survival strategy for owner-managers during the market disruption arising from a crisis situation features as an original contribution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".